This project complements studies of pathophysiology by addressing fractures, the principle clinical manifestation of osteoporosis. Through the unique data resources of the Rochester Epidemiology Project, we can identify large inception cohorts of Rochester and/or Olmsted County, Minnesota., residents with specific medical and surgical conditions and conduct a series of retrospective (=historical) cohort studies to estimate the long-term risk of age-related fractures associated with secondary osteoporosis, an important contributor to bone loss in the elderly. Secondary, osteoporosis is an important area of research because new therapies are being developed for affected men and older women who are not candidates for estrogen replacement. We previously determined the risk of fracture among cohorts with diabetes mellitus, hyperparathryoidism, thyroidectomy, gastrectomy, pernicious anemia, oophorectomy, urolithiasis, anticoagulant therapy, anorexia nervosa, dementia, parkinsonism, epilepsy, poliomyelitis, rheumatoid arthritis, ankylosing spondylitis and breast cancer. We now proposed to extend this work by quantifying the fracture risk associated with conditions that might impair peak bone mass (endometriosis, infertility), induce hypogonadism (orchiectomy), disturb extraskeletal bone metabolism in the kidney (chronic renal failure) and gut (inflammatory bowel disease) or cause a generalized increase in bone resorbing cytokine (multiple myeloma). Each condition represents a natural experiment with respect to the pathogenesis of osteoporosis, several of which parallel the concerns of other projects. These will be the first assessments of fracture risk among cohorts of unselected patients from the community, and the results should be more valid and more precise than any previous estimates. Our overall goal is to develop new information that will lead to effective strategies for preventing osteoporosis-related fractures among the elderly. This project contributes by demonstrating the public health importance of specific risk factors and, by identifying high risk groups within each cohort, allowing future control programs to be designed and conducted more efficiently.